Have any question ?
+91 8106-920-029
+91 6301-939-583
team@appliedaicourse.com
Register
Login
COURSES
Applied Machine Learning Course
Diploma in AI and ML
GATE CS Blended Course
Interview Preparation Course
AI Workshop
AI Case Studies
Courses
Applied Machine Learning Course
Workshop
Case Studies
Job Guarantee
Job Guarantee Terms & Conditions
Incubation Center
Student Blogs
Live Sessions
Success Stories
For Business
Upskill
Hire From Us
Contact Us
Home
Courses
Applied Machine Learning Online Course
Using Web-APIs in Python for Machine Learning
Using Web-APIs in Python for Machine Learning
Instructor:
Applied AI Course
Full Screen
Close
Notes, Colab-notebooks and other files used in this session:
https://drive.google.com/drive/folders/1Dcegfgz4ttmm_jiicePDUmqqCwgPKiMt?usp=sharing
Prev
Next
How to code effectively and build a web-scraper
How to use Github?
55 Comment(s)
Loading...
Search
Login to comment
How to utilise Appliedaicourse
1.1
How to Learn from Appliedaicourse
35 min
1.2
How the Job Guarantee program works
16 min
Python for Data Science Introduction
2.1
Python, Anaconda and relevant packages installations
23 min
2.2
Why learn Python?
4 min
2.3
Keywords and identifiers
6 min
2.4
comments, indentation and statements
9 min
2.5
Variables and data types in Python
32 min
2.6
Standard Input and Output
7 min
2.7
Operators
14 min
2.8
Control flow: if else
10 min
2.9
Control flow: while loop
16 min
2.10
Control flow: for loop
15 min
2.11
Control flow: break and continue
10 min
Python for Data Science: Data Structures
3.1
Lists
38 min
3.2
Tuples part 1
10 min
3.3
Tuples part-2
4 min
3.4
Sets
16 min
3.5
Dictionary
21 min
3.6
Strings
16 min
Python for Data Science: Functions
4.1
Introduction
13 min
4.2
Types of functions
25 min
4.3
Function arguments
10 min
4.4
Recursive functions
16 min
4.5
Lambda functions
8 min
4.6
Modules
7 min
4.7
Packages
6 min
4.8
File Handling
23 min
4.9
Exception Handling
15 min
4.10
Debugging Python
15 min
Python for Data Science: Numpy
5.1
Numpy Introduction
41 min
5.2
Numerical operations on Numpy
41 min
Python for Data Science: Matplotlib
6.1
Getting started with Matplotlib
20 min
Python for Data Science: Pandas
7.1
Getting started with pandas
8 min
7.2
Data Frame Basics
9 min
7.3
Key Operations on Data Frames
31 min
Python for Data Science: Computational Complexity
8.1
Space and Time Complexity: Searching for a number in a list
20 min
8.2
Binary search
17 min
8.3
Find elements common in two lists
6 min
8.4
Find elements common in two lists using a Hashtable/Dict
12 min
SQL
9.1
Introduction to Databases
22 min
9.2
Why SQL?
15 min
9.3
Execution of an SQL statement.
7 min
9.4
IMDB dataset
12 min
9.5
Installing MySQL
11 min
9.6
Load IMDB data.
4 min
9.7
USE, DESCRIBE, SHOW TABLES
15 min
9.8
SELECT
20 min
9.9
LIMIT, OFFSET
10 min
9.10
ORDER BY
6 min
9.11
DISTINCT
10 min
9.12
WHERE, Comparison operators, NULL
13 min
9.13
Logical Operators
27 min
9.14
Aggregate Functions: COUNT, MIN, MAX, AVG, SUM
8 min
9.15
GROUP BY
13 min
9.16
HAVING
12 min
9.17
Order of keywords.
4 min
9.18
Join and Natural Join
12 min
9.19
Inner, Left, Right and Outer joins.
23 min
9.20
Sub Queries/Nested Queries/Inner Queries
24 min
9.21
DML:INSERT
7 min
9.22
DML:UPDATE , DELETE
6 min
9.23
DDL:CREATE TABLE
12 min
9.24
DDL:ALTER: ADD, MODIFY, DROP
4 min
9.25
DDL:DROP TABLE, TRUNCATE, DELETE
3 min
9.26
Data Control Language: GRANT, REVOKE
10 min
9.27
Learning resources
3 min
Module 1: Live sessions
10.1
Code Walkthrough: Basic programming & bug-fixing in Python (for AI)
10.2
Code Walkthrough: Numerical algorithms using Python (for AI)
10.3
Code Walkthrough: Numerical methods in Python (for AI) -II
10.4
Code Walkthrough: Problems in Python [ Strings and Regex ]
10.5
Code Walkthrough: Problems in Python [ Strings and Regex -II]
10.6
Code Walkthrough: Dynamic Programming & Python in-built data-structures
10.7
Code Walkthrough: OOP in Python (for AI)- I
10.8
Code Walkthrough: OOP in Python for AI -II
10.9
Code Walkthrough: Pandas in Python
10.10
Code Walkthrough: Pandas& NumPy- II
10.11
How to code effectively and build a web-scraper
10.12
Using Web-APIs in Python for Machine Learning
10.13
How to use Github?
10.14
Multi-Processing & Multithreading in Python for AI/ML
10.15
Parallel programming for training and productionization of ML/AI systems [Flask & Gunicorn]
10.16
SQL: Importance and Sample Problems
10.17
Interactive Interview Session on Python programming for ML/AI
10.18
Programming problems for AI/ML/DataScience
10.19
Smart data acquisition for ML and AI
10.20
MongoDB for Documents (NoSQL)- Part 1
10.21
MongoDB for Documents (NoSQL)- Part 2
Close